Images can be generated with overlays indicating an amount of brain tissue damage based on the disruption of blood supply. Imaging data can be analyzed to determine perfusion parameters with respect to regions of the brain of an individual. The thresholds for the perfusion parameters with respect to the presence of damaged brain tissue can be based on a period of time elapsed since the onset of a biological condition disrupting blood flow to one or more regions of the brain of the individual. The imaging data can also be analyzed to determine measures of hypodensity with respect to regions of the brain of the individual. A likelihood of the measures of hypodensity corresponding to damaged brain tissue can also be determined based on the period of time elapsed since the onset of the biological condition.

A method and a system of generating a color-coded image demonstrating blood flow in a chosen area in a patient, specifically for diagnosing medical conditions like ischemia, strokes and infarcts. A plurality of images, preferably 3, are taken at different points in time that are a few seconds apart, and colorized by different colors, preferably a first image red, a second image blue and a third image green. These colorized images are then aligned and summated to create a multicolor image that time encodes perfusion of blood. Preferably, said method is performed after injection of a contrast agent.

An ischemic stroke mimic is detected, or otherwise predicted, based on medical images acquired from a subject. Medical image data, which include medical images acquired from a head of the subject, are accessed with a computer system. A machine learning model (e.g., one or more deep convolutional neural networks) is trained on training data to estimate a probability of an acute intracranial abnormality being depicted in a medical image. Intracranial abnormality prediction data are generated by inputting the medical image data to the machine learning model. The intracranial abnormality prediction data include an intracranial abnormality probability score for each of the medical images in the medical image data. An ischemic stroke mimic classification for the medical image data is generated based on the intracranial abnormality prediction data, and may be displayed to a user with the computer system.

Apparatus and methods are described for use with an endoluminal device that includes one or more radiopaque portions and that moves through a lumen of a subject. A sequence of radiographic images of a portion of the subject's body, in which the lumen is disposed, is acquired, during movement of the endoluminal device through the lumen. Locations at which the one or more radiopaque portions of the endoluminal device were imaged during the movement of the endoluminal device through the lumen are identified, by analyzing the sequence of radiographic images. A set of locations at which the one or more radiopaque portions were disposed during the movement of the endoluminal device through the lumen is defined, and an endoluminal path of the device through the lumen is estimated based upon the set of locations. Other applications are also described.

Systems and methods are disclosed for automatically performing motion correction in dynamic positron emission tomography scans, such as dynamic positron emission tomography myocardial perfusion imaging studies. An automated algorithm can be used. The algorithm can use simplex iterative optimization of a count-based cost-function customized to different dynamic phases for performing frame-by-frame motion correction.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Systems and methods for displaying flow index values on a user interface. An example method may include receiving medical images imaging a portion of a vasculature of a subject, with the portion of the vasculature including vessels; producing, by automatic processing of the medical images, a three-dimensional vascular model of the portion of the vasculature comprising the one or more vessels based on the medical images; calculating flow index values quantifying vascular function along each of the one or more vessels based on the three-dimensional vascular model; displaying a representation of the three-dimensional vascular model comprising the one or more vessels; and for a designated vessel of the one or more vessels, simultaneously displaying the flow value index for a designated location of the designated vessel along with the flow value index for a predetermined distal location along a length of the designated vessel.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

A system and method for determining a trajectory parameter of particles, comprising receiving a plurality of particles at a microfluidic channel, applying a force to each particle of the microfluidic channel, acquiring a dataset of each particle, measuring a trajectory of the particle, and determining a trajectory parameter of the particles.

A method for computing cerebral blood flow data according to an embodiment of the present disclosure includes the steps of: acquiring a trained cerebral blood flow prediction model; acquiring an original medical image; acquiring morphological data corresponding to the cerebral blood vessel region from the original medical image, the morphological data being composed of data in the form of a point cloud; acquiring a boundary area of the cerebral blood vessel region and acquiring boundary information corresponding to the boundary area; acquiring initial condition information or boundary condition information; and inputting the morphological data, the boundary information, and the initial condition information or boundary condition information into the cerebral blood flow prediction model, and acquiring cerebral blood flow data related to the speed or pressure of the cerebral blood flow output through the cerebral blood flow prediction model.